Energy Consumption Ratio Research Articles

An Adaptive Fuzzy C Means with Seagull Optimization Algorithm for Analysis of WSNs in Agricultural Field with IoT

In recent years, the environmental monitoring in agriculture field is an essential required application. To achieve the environmental monitoring of agriculture fields, the wireless sense networks (WSN) and internet of things is utilized. In the WSN, the energy consumption is a main issue to access the medium and transfer the networks. Hence, in this paper, adaptive fuzzy C means clustering and seagull optimization algorithm is developed for monitoring environmental conditions in agriculture field. Two main objective functions are utilized to empower the presentation of the WSN such as load balancing and energy efficient operation. The proposed method is a combination of fuzzy C means clustering and seagull optimization algorithm (SOA). The energy efficient and load balancing is achieved by optimal routing scheme by proposed method. The fuzzy C-means clustering is utilized to empower the energy efficient operation and load balancing. In the fuzzy C-means clustering, the SOA is utilized to select the optimal path selection. The proposed method is executed by NS2 simulator and performances are compared with existing methods such as atom search optimization and emperor penguin optimization respectively. The performance metrics are delay, drop, throughput, energy consumption, network lifetime, overhead and delivery ratio.

Clean to dirty energy consumption ratio, institutional quality, and welfare; an inter-continental panel quantile analysis

Since the end of the 20th century, research about the type of energy consumption has grown dramatically. Nevertheless, in previous studies there were two neglected essential questions; “Why does the desire for clean energy vary across the country?” and “What factors increase the desire for clean energy?”. This study was conducted to investigate the impact of institutional quality and gross domestic product per capita as a proxy of welfare on the clean to dirty energy consumption ratio among 75 countries over the period 1996–2018. Here, the energy source from which carbon emissions are negligible or zero is called clean energy. In contrast, the energy source with significant carbon emissions is called dirty energy. The results of the random-effect panel quantile regression indicate positive and significant effects of rule of law on the clean to dirty energy consumption ratio. As, in the first decile (polluted countries from the perspective of energy consumption) by increasing one unit in rule of law, the clean to dirty energy consumption ratio shall increase by 0.027. Also, welfare has a positive and significant effect on the clean to dirty energy consumption ratio. Therefore, if gross domestic product per capita increases by one unit (equal to a thousand dollars), then the clean to dirty energy consumption ratio will increase by 0.0022, in the first decile. Nevertheless, the initial conditions of countries such as the existence of oil or gas resources, the capacity to generate nuclear power, and the existence of water resources are the most influential factors on the clean to dirty energy consumption ratio. Therefore, one of the appropriate and long-run policies for the transition from communities based on dirty energies consumption to communities based on clean energies consumption is to improve the institutional quality and the welfare level of communities.

Game theory based hybrid localization technique for underwater wireless sensor networks

AbstractIn an underwater environment, it is necessary but tough to track the location of deployed sensor nodes because of node's mobility, nonavailability of GPS, node software or hardware failure and so forth. Due to these reasons, nodes cannot directly communicate with enough beacons in the neighbourhoods, resulting into localization error. This creates the need to develop the range‐based localization method that takes into consideration the features of the underwater environment with precise location estimation. The existing range‐based localization techniques involve huge computational and storage overhead along with localization latency. To solve these problems, a hybrid localization estimation technique of received signal strength (RSS) and angle of arrival (AoA) is presented in this article. In this hybrid method, AoA is estimated from the primary anchor (PA) while RSS is measured from the selected set of secondary anchors (SAs). Further, Game theory model is also applied to considerably increase the precision by choosing the optimum set of SAs even in the mobility assisted underwater environment. The proposed technique of identifying unknown sensor nodes is tested through NS2 simulation with multiple runs. The proposed technique achieves minimum localization error, delay, energy consumption and high packet delivery ratio in comparison to topology control for energy‐efficient localization, game theory based routing protocol, iterative mobile target localization, and RSS‐based localization.

EATMR: an energy-aware trust algorithm based the AODV protocol and multi-path routing approach in wireless sensor networks

Rapid developments in radio technology and processors have led to the emergence of small sensor nodes that provide communication over Wireless Sensor Networks (WSNs). The crucial issues in these networks are energy consumption management and reliable data exchange. Due to the limited resources of sensor nodes, WSNs become a vulnerable target against many security attacks. Thus, energy-aware trust-based techniques have become a powerful tool for detecting nodes’ behavior and providing security solutions in WSN. Clustering-based routings are one of the most effective methods in increasing the WSN performance. In this paper, an Energy-Aware Trust algorithm based on the AODV protocol and Multi-path Routing approach (EATMR) is proposed to improve the security of WSNs. EATMR consists of two main phases: firstly, the nodes are clustered based on the Open-Source Development Model Algorithm (ODMA), and then in the second phase, clustering-based routing is applied. In this paper, the routing process follows the AODV protocol and multi-path routes approach with considering energy-aware trust. Here, the optimal and safe route is determined based on various parameters, namely energy, trust, hop-count, and distance. In this regard, we emphasize the evaluation of node trust using direct trust, indirect trust, and a multi-objective function. The simulation has been performed in MATLAB software in the presence of a Denial of Service (DoS) attack. The simulation results show that EATMR performs better than the state-of-the-art methods in terms of successfully detecting malicious nodes and enhancing network lifetime, energy consumption, and packet delivery ratio. As a conclusion, EATMR shows an average of 4.3 and 6.1% superiority over M-CSO and SQEER in different scenarios, respectively.

Optimal use of glycerol co-solvent to enhance product yield and its quality from hydrothermal liquefaction of refuse-derived fuel.

Refuse-derived fuels (RDF) are rich in resources that make them an attractive feedstock for the production of energy and biofuels. Hydrothermal liquefaction (HTL) is a promising thermochemical conversion technology to handle wet feedstocks and convert them to valuable bio-crude, bio-char and aqueous products. This study highlights the advantages of using glycerol as the co-solvent along with water in different proportions to produce bio-crude from RDF via HTL. The ratio of water:glycerol (vol.%:vol.%) was varied for each experiment (100:0, 90:10, 80:20, 70:30, 60:40, 50:50), and the product yields and their quality were studied. The results demonstrate that increasing the proportion of glycerol until 50 vol.% in the solvent enhances the bio-crude yield (36.2 wt.%) and its higher heating value (HHV) (30.9 MJ kg−1). Deoxygenation achieved in the bio-crude was 42%. The production of bio-char was minimum (9.5 wt.%) at 50 vol.% glycerol with HHV of 31.9 MJ kg−1. The selectivity to phenolic compounds in the bio-crude increased, while that of cyclic oxygenates decreased when the glycerol content was more than 20 vol.%. The gas-phase analysis revealed that the major deoxygenation pathway was decarboxylation. The yield of aqueous products drastically increased with the addition of glycerol. The minimum amount of glycerol in the co-solvent that favours an energetically feasible process with low carbon footprint is 30 vol.%. Using 50 vol.% glycerol resulted in the highest energy recovery in the bio-crude and bio-char (80%), the lowest energy consumption ratio (0.43) and lowest environmental factor (0.1). The mass-based process mass intensity factor, calculated based on only bio-crude and bio-char as the valuable products, decreased with an increase in addition of glycerol, while it was close to unity when the aqueous phase is also considered as a valuable product.

Energy supply and influencing factors of mountain marathon runners from Baiyin marathon accident in China

High temperature impacts the performance of marathon athletes, and hypothermia harms athletes. Twenty-one runners died, and eight were injured in the China Baiyin marathon on May 22, 2021. It’s a typical human life test. The energy equations are combined with the maximum energy supply of Chinese male athletes to study this accident. We analyze the human body’s route slope, travel speed, and heat dissipation under low temperatures in this marathon. The study shows that the large slope and long-distance of CP2 to CP3 section and the low temperature during the competition are the main reasons for the accident. The method of quantifying the slope and temperature and calculating the percentage of athletes’ physical consumption proposed in this paper can evaluate the route design of field marathons. We suggest that the physical energy consumption ratio of 90%, i.e. 315 cal/min/kg, should be taken as the maximum energy supply for Chinese male marathon runners. Dangerous risk zones for wind speed and temperature on dangerous path sections are also formulated for athletes to make their assessments. This paper’s theories and methods can effectively help design the marathon route and determine the race time.

R-ARM: Retransmission-Assisted Resource Management in LoRaWAN for the Internet of Things

LoRaWAN exhibits an essential feature, namely, the adaptive data rate (ADR), which has been recommended for the management of resources (e.g., the spreading factor and transmit power) of static end devices (EDs) based on channel conditions. Blind ADR (BADR) has been introduced for LoRaWAN mobile applications that experience frequent channel attenuation when the ED moves (e.g., pet-tracking). This channel condition leads to massive packet loss and retransmission, which significantly increases energy consumption. In this study, ADR and BADR are investigated in mobility environments, their limitations are highlighted, and a novel ADR “retransmission-assisted resource management (R-ARM)” system is proposed. The proposed R-ARM system operates concurrently on the ED and network server sides. This improves the network performance of the LoRaWAN. When compared to those of typical ADR approaches, the simulation results, in this case, show that R-ARM significantly enhances the packet success ratio and convergence period, and it lowers the energy consumption and packet loss ratio.

Earthquake and household energy consumption – Evidence from the Wenchuan earthquake in China

In this article, we focus on how household consumption responded to the Wenchuan earthquake in China. Based on the data from 2007, 2008 and 2009 from the China Urban Household Survey, we adopt the difference-in-difference model (DID) and find that the Wenchuan earthquake significantly restrained household consumption. First, the total consumption, energy consumption and energy consumption ratio were suppressed by the Wenchuan earthquake. The negative impact was mainly driven by the “living like there's no tomorrow” effect, and the consumption decisions of the affected households were not always identical. A possible mechanism is salience theory. Second, both the energy consumption ratio and fuel consumption ratio were dramatically reduced by the Wenchuan earthquake. Third, transfer payments helped households mitigate adverse impacts. In addition, households with low education, low income and low social networks were the most strongly affected by the Wenchuan earthquake.

Enhanced Communication in Void Region of Underwater Wireless Sensor Network using Vector-Based Void Avoidance Comparing with Void Aware Pressure Routing Protocol

The aim of the proposed work is to improve the energy efficiency of sensor nodes in underwater wireless sensor networks using Vector-based void avoidance (VBVA) in comparison with void-aware pressure routing (VAPR). The performance of vector-based void avoidance uses the vector shift and backpressure mechanisms compared with the void-aware pressure routing protocol. A total of 34 samples are collected and 17 samples for each group is taken to the data sets. The proposed algorithm is tested in an underwater virtual environment by Aquasim in the NS2 simulator. The throughput, energy consumption, and packet delivery ratio are calculated to evaluate the performance of the proposed VBVA Protocol. The energy efficiency of the proposed vector-based void avoidance protocol is improved by avoids of void nodes and forward data packets. The Throughput mean value is (1.3297), Energy consumption mean value is (0.9960) and Packet delivery ratio mean value is (91.1735) achieved using VBVA protocol. This work has obtained significance (<0.01). The simulation results show that reduction of packet loss and improve the energy efficiency of the sensor network using the proposed VBVA algorithm in comparison with VAPR.

Co-liquefaction of organic solid waste with fecal sludge for producing petroleum-like biocrude for an integrated waste to energy approach

Globally, approximately 600–700 million tonnes/yr. of solid waste (SW) and 250–300 million tonnes/yr. of fecal sludge (FS) are not managed in an environmentally safe manner. Conversion of these two waste streams using this ratio (2–3: 1 of SW: FS) to produce biocrude for fossil fuel substitution would act as an integrated waste to energy approach to address current global crises (GHGs emissions, climate change, and global warming). This study took the opportunity to produce petroleum-like biocrude through co-liquefaction of organic solid waste (OSW) and fecal sludge (FS) and optimized crude production yield and quality by varying feedstock ratios (1:0, 1:3, 1:1, 3:1, and 0:1) and temperature (280–340 °C). A higher synergistic effect was observed at feedstock ratio 3:1 (OSW: FS) and 320 °C with 52% biocrude yield, of which 64% was lighter-fraction (C < 20). The produced light crude contained 60% ester, 14% heterocyclic, and 12% fuel hydrocarbon fractions and exhibited a heating value of 42.7 MJ/kg, suggesting a potential candidate for petro crude (42–49 MJ/kg) substitution. This energetically feasible process (energy recovery 86% and energy consumption ratio 0.36) would provide a revenue of US$ 467.8 per metric ton of waste feedstock, validating the high economic prospects for a cleaner production pathway.

Fuzzy-assisted machine learning framework for the fog-computing system in remote healthcare monitoring

Health care monitoring systems have mainly depended on the internet of things (IoT) devices to collect, manage and analyze data from patients. Patients' health can be constantly monitored and controlled with the remote health monitoring systems. From these points, a Fuzzy-assisted machine learning framework (F-AMLF) with fog computing increases the effectiveness of the health care monitoring system. This paper presents an F-AMLF to recognize how device resource cost reduction is achieved while maintaining efficiency limitations. Patients can submit their demands for health care by a fuzzy assisted fog computing system. These systems use fuzzy logic to calculate how much computing capacity is needed to maintain fog and cloud projections. The F-AMLF shows the highest accuracy ratio of 93.6%, monitoring ratio of 92.5%, prediction ratio of 95.3%, data management ratio of 91.4%, and the lowest latency ratio of 19.7%, energy consumption ratio of 20.1%, and the cost-effective ratio of 21.5% compared to the existing methods.

Review of ALOHA-based MAC Protocols for Underwater Wireless Sensor Networks. (Dept.E. Computer)

The aquatic environment has become clearer and more controllable since Underwater Wireless Sensor Networks (UWSNs) intervened to detect them, because underwater wireless sensor networks are efficiently providing and supporting many civilian and military applications, and many have been decomposed and extracted for helping to avoid falling into many problems, causing many physical and economic losses. Media Access Control (MAC) protocol plays a fit role to enhance the performance of the network which helps to quickly accomplish the tasks that are required from the network and without additional cost. Low and limited bandwidth, energy, limited memory, long and variable propagation delay, and high bit error rate are some challenges that face the designing of the MAC protocol for UWSNs. ALOHA protocol is one of the most popular MAC protocols. In this article, we focus on providing a review of the state of art of the most recent developments of ALOHA protocols for UWSNs from recent literature. These protocols are P-ALOHA, S-ALOHA, ALOHA–CS, ALOHA-AN, BUFFERED-ALOHA, SLOTTED-CS-ALOHA, VI-ALOHA, L-ALOHA, ST-SLOTTED-CS-ALOHA, MODIFIED-SLOTTED-ALOHA, SLOTTED-BUFFERING-ALOHA, and BUFFERING_SLOTTED_ALOH. A discussion of the characteristics and restrictions of every ALOHA protocol in addition to explaining comparisons among all these protocols according to different performance metrics is also presented in this paper. The performance metrics used are the average delay, the energy consumption ratio, the number of dropped nodes, and the throughput ratio.

Shrimp waste-derived chitosan harvested microalgae for the production of high-quality biocrude through hydrothermal liquefaction

Microalgae harvesting is an expensive and energy-intensive process. The application of chitosan, a waste-derived natural flocculant, for microalgae harvesting and biocrude quality enhancement might overcome this challenge. This study explored the optimization of microalgae harvesting by shrimp waste-derived chitosan and the production of high-quality biocrude from the harvested biomass through hydrothermal liquefaction (HTL). The study suggested that more than 95% of the microalgal biomass were harvested at 17–26 mg/L of chitosan dosing with a mixing time and speed of more than 11 min and 75 rpm, respectively. The biocrude produced from chitosan harvested microalgae (CMA) showed higher yield (37.9%) and energy value (35.4 MJ/kg) than gravity harvested microalgae (GMA) (yield 28.2% and HHV 30.3 MJ/kg). In addition, CMA biocrude had higher °API gravity (18.4) and a higher percentage (66%) of lighter hydrocarbon fraction (<C20), which indicated high-quality crude properties. Finally, the higher energy recovery (85.2%) and lower energy consumption ratio (0.58) of CMA biocrude confirmed the net energy positivity system. In this context, the findings demonstrated that applying waste-derived flocculant (chitosan) for wastewater-grown microalgae harvesting and high-quality biocrude production would potentially lead to a sustainable algal biorefinery system.

Intelligent power grid monitoring and management strategy using 3D model visual computation with deep learning

The legacy grid is developing into an intelligently rooted grid system that can enhance and adapt its performance to learning experiences in its architectural components. Due to the evolution of artificial intelligence and in-depth learning techniques, decision-making and execution can become autonomous activities in intelligent power grids throughout the years. 3D visualization plays a vital role in simplifying monitoring, analysis, and reaction to events in the smart grid. The visualization of the 3D models for various energy system applications is first summarized, including the smart grid, power vehicles, and energy consumption of buildings. Accordingly, design principles are provided for wide-screen, personal computer, or mobile interfaces. Visualization technology for geographical information systems is described. 3D technology, animations, and AR&VR to visualize the energy system is discussed. In this article, a coherent 3D visualization approach for the control and monitoring of intelligent power grids (P.G.) via the deep learning (DL) method is examined in industry literature (3D-PGDL). Data visualization is gaining significance with improved measurement systems and Big Data Analytics in intelligent grids and low carbon energy systems. No data design study, accompanying technology, or visualization tools have been conducted. The analysis and practice of data viewing on energy and power systems are thoroughly addressed here. Because of the intrinsic properties of intelligent grids, all collected and processed data are heterogeneous to provide a more comprehensive, multifaceted control viewpoint for the energy sector by creating new ICT instruments and platforms. This section’s experiment resulted in a 95.3 % prediction rate and improved data management ratio of 95.7 %, a lower energy consumption ratio of 18.2%, a lower probability of 18.4 %, a lower CO2 emission level of 15.6 % and a humidity rate of 93.1 %.

A Multiobjective Fractional Programming for a CHP System Operation Optimization Based on Energy Intensity

The objective of this research is to establish a multiobjective fractional programming (MOFP) model for supporting the operational management of a combined heat and power (CHP) system. Compared with the traditional operational optimization model of the CHP system, the importance of the energy intensity (i.e., the ratio of energy consumption and energy production) was emphasized in the MOFP model, which is considered as the system objective for replacing the common objective of minimizing the economic cost. This innovative transformation effectively reduces excessive energy consumption, accompanied by improvement in the system revenue. The CHP system of an industrial park in the City of Jinan, China, was used as a study case for demonstration. The obtained results reflected that the combination of two gas turbines (GTs) ensured safe, efficient, and stable output for meeting daily power requirements in various seasons. As for the steam load, during the summer, two heat recovery steam generators (HRSGs) play a major role, where the insufficient part is supplemented by two gas-fired boilers (SBs); conversely, the steam load in winter is mainly satisfied by the aid of two SBs. The successful application of the MOFP model in the park could provide a good demonstration for CHP management in many other districts and cities.

Energy Evolution and Damage Mechanism of Fractured Sandstone with Different Angles

To explore the influence of crack angle on the mechanical properties, energy evolution, and damage evolution of sandstone, uniaxial loading tests were conducted on sandstones with different crack angles. Through the stress–strain curve, the influence of the crack angle on the mechanical properties was analyzed. Based on energy theories and principles, the influence of crack angle on the energy conversion mechanism was analyzed. Based on crack angle and dissipated energy, a damage model considering the initial damage to the fractured sandstones was established. The following conclusions were drawn: (1) The strength and elastic modulus of sandstone decrease with an increase in crack angle, and Poisson’s ratio increases with an increase in crack angle; prefabricated cracks affect the crack initiation position, and accelerate the formation of fracture surfaces. (2) The stress–strain curve was divided into compaction stage, elastic stage, yield stage, and failure stage. The larger the crack angle, the longer the yield stage and the shorter the failure stage. (3) At the peak point, the elastic energy, dissipated energy, and input energy of fractured sandstone always decrease with an increase in crack angle; the energy consumption ratio increases with an increase in crack angle; and the energy storage ratio decreases with an increase in crack angle. (4) The damage variable shows a trend of slow accumulation–steady accumulation–rapid accumulation; the crack angle affects the initial damage of the specimen, and the dissipated energy affects the variation trend of the damage variable.

A Directional Selective Power Routing Protocol for the Internet of Underwater Things

The Internet of Underwater Things (IoUT) has lately gained popularity as a means of facilitating a wide range of underwater applications. In the IoUT, underwater communication is best accomplished by the usage of acoustic waves, whereas the terrestrial communication between the surface sinks and the on-shore control stations is typically achieved using radio waves. As a result, the greatest portion of an IoUT is enabled by the underwater acoustic sensor network (UASN), where the specific issues provided by the use of acoustic waves, the underwater node mobility, and the localization difficulties have yet to be addressed. In this paper, we discuss the challenges faced by the IoUT in terms of the currently proposed routing protocols and propose a Directional Selective Power Routing Protocol (DSPR) to cope with most of these challenges. The proposed protocol (i.e., DSPR) uses the angle of arrival and the sender depth information to find the best direction to the surface sink. In addition, the DSPR uses selective power control to enhance the delivery ratio and ensure connectivity while reducing energy consumption. To testify the performance of the proposed protocol, intensive simulation experiments have been conducted. The simulation results show that the proposed DSPR protocol outperforms two variations of the fixed directional routing (DR) protocol and the variable power depth-based routing (VDBR) protocol in terms of energy consumption and delivery ratio. For instance, the proposed DSPR protocol achieves at least 8 times enhancement in energy consumption compared with VDBR. In addition, DSPR saves around 30% of energy consumption over the DR protocols when the network is mobile. Moreover, the DSPR protocol acquires a delivery ratio above 90% for static/dynamic scenarios in both sparse and dense networks.

Correction to: A New Rock Brittleness Index Based on the Peak Elastic Strain Energy Consumption Ratio

Correction to: A New Rock Brittleness Index Based on the Peak Elastic Strain Energy Consumption Ratio

A New Rock Brittleness Index Based on the Peak Elastic Strain Energy Consumption Ratio

A New Rock Brittleness Index Based on the Peak Elastic Strain Energy Consumption Ratio

Sustainable Energy Management in Intelligent Transportation

Intelligent transportation systems (ITS) are a collection of technologies that can enhance transport networks and public transit and individual decision-making about various elements of travel. ITS technologies comprise cutting-edge wireless, electronic and automated technology intending to improve safety, efficiency and convenience in surface transit. In certain cases, reducing energy usage has proven to be an ITS advantage. In this report, the primary energy advantages of a range of ITS systems established through models, pilot projects/field tests and extensive use are examined and summarized. In worldwide driving, the Internet of Things (IoT) solutions play a vital role. A new age of communication leading to ITS will be the communication between cars via IoT. IoT is a mixture of data and data analysis data storage and processing to manage the traffic system efficiently. Energy management, which is seen as an efficient, innovative approach to highly efficient energy generation plants. It simultaneously takes care of optimizing traditional sources of the IoT based intelligent transport system, helps to automate railways, roads, airways and shipways, which improve customer experience in the process. Following an evaluation of the situation, a proposal named energy management in intelligent transportation (EMIT) improves energy efficiency and economic efficiency in transportation. It improves energy management to reduce economic and ecological waste by decreasing global transport energy consumption. The sustainable development ratio is 85.7%, accidents detection ratio is 85.3%, electric vehicle infrastructure ratio is 83.6%, intelligent vehicle parking system acceptance ratio is 82.15%, and reduction ratio of energy consumption is 91.4%.